/A Discriminative Framework for Anomaly Detection in Large Videos

A Discriminative Framework for Anomaly Detection in Large Videos

Allison Del Giorno, J. Andrew (Drew) Bagnell and Martial Hebert
Conference Paper, European Conference on Computer Vision (ECCV), October, 2016

Download Publication (PDF)

Copyright notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author’s copyright. These works may not be reposted without the explicit permission of the copyright holder.


We address an anomaly detection setting in which training sequences are unavailable and anomalies are scored independently of temporal ordering. Current algorithms in anomaly detection are based on the classical density estimation approach of learning high-dimensional models and finding low-probability events. These algorithms are sensitive to the order in which anomalies appear and require either training data or early context assumptions that do not hold for longer, more complex videos. By defining anomalies as examples that can be distinguished from other examples in the same video, our definition inspires a shift in approaches from classical density estimation to simple discriminative learning. Our contributions include a novel framework for anomaly detection that is (1) independent of temporal ordering of anomalies, and (2) unsupervised, requiring no separate training sequences. We show that our algorithm can achieve state-of-the-art results even when we adjust the setting by removing training sequences from standard datasets.

BibTeX Reference
author = {Allison Del Giorno and J. Andrew (Drew) Bagnell and Martial Hebert},
title = {A Discriminative Framework for Anomaly Detection in Large Videos},
booktitle = {European Conference on Computer Vision (ECCV)},
year = {2016},
month = {October},
publisher = {Springer},